Contributions: this article attempts to review the contribution of vector representations to
measure sentence similarity. We compare them with similarity measures based on lexical resources such as WordNet or DBnary.
Machine Translation (MT) evaluation was identified as a particularly interesting application to investigate, since MT evaluation is still an open problem nowadays. More precisely, we propose to augment a well known MT evaluation metric (METEOR [1]}) which allows an approximate matching
(through synonymy or morphological similarity) between MT hypothesis and reference. The augmented versions of METEOR proposed (using word embeddings, lexical resources or both) allow us to objectively compare the contribution of each approach to measure sentence similarity. For this, correlations between METEOR and human judgements (of MT outputs) are measured within the framework of WMT 2014 Metrics task. The code of the augmented versions of METEOR is also provided on our Github pagehttps://github.com/cservan/METEOR-E.
